Work

Benchmark and Survey of Automated Machine Learning Frameworks

Year: 2021

Type: article

Abstract: Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to build machine learning applications automatically without extensive knowledge of statistics and machine learning. This paper is a combination of a survey on current AutoML methods and a benchmark of popular AutoML frameworks on real data sets. Driven by the selected frameworks for evaluation, we summarize and review important AutoML techniques and methods concerning every step in building an ML pipeline. The selected AutoML frameworks are evaluated on 137 data sets from established AutoML benchmark suites.

Source: Journal of Artificial Intelligence Research

DOI: 10.1613/jair.1.11854

Authors: Marc-André Zöller, Marco F. Huber

Institutions: University of Stuttgart, Fraunhofer Institute for Manufacturing Engineering and Automation, Institute of Electrical and Electronics Engineers

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Bundesministerium für Verkehr und Digitale Infrastruktur

OpenAlex ID: W2999356315

FWCI: 26.3

Cites: 235

Cited by: 448

Related to: 20